Methods and systems for allocation of macro cell resources in a distributed femto cell network and a distributed relay station network

ABSTRACT

A method implemented in a wireless network including a first base station and a second base station is disclosed. The method includes, upon a change in a load condition, transmitting from the second base station to the first base station load information through backhaul in order to coordinate the first base station and the second base station, allocating, to the second base station, one or more tiles corresponding to one or more time-frequency resource blocks of orthogonal frequency division multiple access (OFDMA), and allocating remaining tiles to the first base station. Other methods and apparatuses are also disclosed.

RELATED APPLICATION INFORMATION

This application is a divisional of co-pending U.S. patent applicationSer. No. 12/567,163, filed on Sep. 25, 2009, which claims priority toprovisional application Ser. No. 61/114,200, filed on Nov. 13, 2008,incorporated herein by reference.

BACKGROUND

1. Technical Field

The present invention relates to allocation of resources to femto cellbase stations and relay stations and, more particularly, to allocationof time-frequency blocks of orthogonal frequency division multipleaccess (OFDMA) frames to femto cell base stations and relay stations.

2. Description of the Related Art

Femto cells are a cost-effective means of providing ubiquitousconnectivity in future broadband wireless networks. In general, femtocells have primarily been used to improve coverage in current businesssolutions. There has been a significant impetus towards the deploymentof these femto cell solutions by several cellular providers, which hasalso increased their importance for consideration in the futureOFDMA-based standards such as Long Term Evolution (LTE) and for use intechnologies such as World Interoperability for Microwave Access(WiMAX).

Current UMTS-based femto solutions have focused on improving indoorcoverage in existing cellular systems. Here, users that are moved tofemto cells experience increased throughput due to shorter ranges. Inturn, users communicate to the macro cell base station via the femtocells by using a cable backhaul between femto cells and the macro cellbase station. UMTS-based femto cell solutions are similar to other femtocell solutions in that they are driven primarily from a businessincentive perspective, where the ability to reuse cable backhaul helpsprovide extended coverage to users, thereby reducing user-churn fornetwork providers. Hence, current UMTS-based femto solutions mainlyfocus only on interference mitigation between macro and femto cellsthrough way of power control.

An architecture that is analogous to some degree to femto cell systemscurrently used is that of wireless local area network (WLAN) hotspotsinside a macro cell. Several works have examined problems pertaining tohandoff, coverage, etc. in the combined wireless wide area network(WWAN)-WLAN architecture. However, the varied technologies and spectrumsemployed decouple the resource management problem in the two separatenetworks using two different sets of resources. The WLAN hotspottechnologies do not consider managing shared, common resources betweendifferent types of networks.

With regard to distributed channel allocation schemes, WLAN technologiesonly focus on access points converging to a single mutually orthogonalchannel for operation, after which the allocation can be staticallyretained. Medium access control (MAC) in ad-hoc networks offer anothermeans for resource allocation in distributed operations. However, theyare typically variants of 802.11 MAC protocol that use control messagesin aiding the distributed control.

SUMMARY

Exemplary embodiments of the present invention address the deficienciesof the prior art by providing an efficient means for reusing macro cellresources at femto cell base stations that can dramatically enhancesystem performance. In accordance with one exemplary aspect of thepresent invention, a set of femto cell base stations can individuallyreuse and allocate macro cell resources in a completely distributed andnon-collaborative manner that exhibits fast convergence properties andhas performance guarantees. Further, in accordance with other exemplaryaspects, allocation of orthogonal resources between femto cell basestations and a macro cell base station may be dynamically adjusted basedon user-population variance. In particular, fast convergence of anoptimal split may be obtained by utilizing an intelligent initial splitpoint that is adapted to the specific type of network architectureemployed in accordance with the teachings described herein below.

It should be understood that an “extension cell base station” is herebydefined as being either a femto cell base station or a relay station. Afemto cell base station employs a cable backhaul to communicate with amacro cell base station or a network controller while a relay stationemploys a wireless backhaul to communicate with a macro cell basestation or a network controller. It should also be understood that,except for the use of a cable backhaul, all aspects described hereinwith respect to femto cell base stations can be applied to relaystations.

In one exemplary embodiment of the present invention, a method foruncoordinated allocation of tiles corresponding to time-frequencyresource blocks of OFDMA frames between distributed extension cell basestations, that may be performed at a given extension cell base stationincludes: selecting a prime number (P) based on a total number (N) ofextension cell base stations contending for said tiles; generating ahash function by randomly choosing a hash vector with componentsselected from a hash table of size P, and dividing said tiles into setsof P tiles each; allocating tiles from said sets of P tiles to the givenextension cell base station by applying the hash function to each set;and assigning said allocated tiles to clients of the given extensioncell and transmitting assignment messages to said clients to permitcommunication on said allocated tiles.

In an alternate exemplary embodiment of the present invention, a methodfor dynamic allocation of tiles corresponding to time-frequency resourceblocks OFDMA frames between a macro cell base station and a set ofdistributed extension cell base stations within the macro cell based onuser-population variance includes: detecting that a change in auser-population serviced by at least one of the macro cell base stationor the set of distributed extension cell base stations exceeds athreshold; performing an initial allocation in which the set ofextension cell base stations are allocated

$\frac{NF}{F + M}$

of said tiles, wherein N is the total number of said tiles, F is thetotal number of extension cell users and M is the total number of macrocell users, and in which the macro cell base station is allocatedremaining tiles; varying the number of tiles allocated to said set ofextension cell base stations; and iterating said varying until a utilitymeasure for said macro and extension cell base stations is optimized.

In an another exemplary embodiment of the present invention, a methodfor dynamic allocation of tiles corresponding to time-frequency resourceblocks of OFDMA frames between a macro cell base station and a set ofdistributed extension cell base stations based on user-populationvariance includes: detecting that a change in a user-population servicedby at least one of the macro cell base station or the set of distributedextension cell base stations exceeds a first threshold; performing aninitial allocation in which the set of extension cell base stations areallocated

$\frac{{NF}_{i}}{F + M}$

of said tiles, wherein N is the total number of said tiles, F is thetotal number of extension cell users, M is the total number of macrocell users and F_(i) is the number of extension cell users that contendwith interference exceeding a second threshold stemming from macro cellbase station transmissions, and in which the macro cell base station isallocated remaining tiles; varying the number of tiles allocated to saidset of extension cell base stations; and iterating said varying until autility measure for said macro and extension cell base stations isoptimized.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a diagram illustrating a base station system in accordancewith one exemplary embodiment of the present invention.

FIG. 2 is a block diagram illustrating a split of orthogonal resourcesof an OFDMA frame between a macro cell base station and femto cell basestations in accordance with one exemplary embodiment of the presentinvention.

FIG. 3 is a block/flow diagram illustrating a method for distributedallocation of tiles between femto cell base stations in accordance withexemplary embodiments of the present invention.

FIG. 4 is a block/flow diagram illustrating a method for tile collisionresolution between femto cell base stations in accordance with oneexemplary embodiment of the present invention.

FIG. 5 is a diagram illustrating interference scenarios and detectioncapabilities by employing interference degree and largest maximal cliquesize of interfering femto cell base stations.

FIG. 6 is a block/flow diagram illustrating a method for tile collisionresolution between femto cell base stations that considers idle tiles inaccordance with one exemplary embodiment of the present invention.

FIG. 7 is a block/flow diagram illustrating a method for splittingresources between a macro cell base station and femto cell base stationsin an isolated architecture based on user-population variance inaccordance with one exemplary embodiment of the present invention.

FIG. 8 is a block/flow diagram illustrating a method for splittingresources between a macro cell base station and femto cell base stationsin a coupled architecture based on user-population variance inaccordance with an alternative exemplary embodiment of the presentinvention.

FIG. 9 is a block/flow diagram of an exemplary location-based resourceallocation method for allocating tiles to macro cell users in accordancewith an exemplary implementation of the present invention.

FIG. 10 is a block/flow diagram of an exemplary location-based resourceallocation method for allocating tiles to femto cell users in accordancewith an exemplary implementation of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

As noted above, the primary purpose of femto cells has been to improvecoverage in business solutions. Their decreased cell sizes in turn mayalso provide improved cell capacity through increased spatial reuse ofmacro cell resources, wherein multiple femto transmissions can beexecuted on the same sub-channel in a macro cell. Femto cell reuse ofmacro cell resources provides benefits to both macro cell users andfemto cells users. For example, femto cell reuse of macro cell resourcesresults in higher femto user throughput, while at the same timeadditional macro users can be accommodated with higher macro throughput,which provides a beneficial situation that has not be well exploited bycurrent solutions. Improved capacity resulting from spatial reuse may beemployed to meet the demand for bandwidth-intensive internet protocol(IP) services such as video streaming, IP television (IPTV),video-on-demand services, etc. Further, such demands can be met usingsystems and methods provided below without having to appreciablyincrease subscription services, thereby reducing the user churn.

A difficult problem with allocating resources to femto cells is the lackof direct coordination between the macro and femto cell base stationsand the completely distributed nature of femto cells. To address theproblem, several efficient resource management solutions for OFDMA-basedfemto cells with performance guarantees are provided herein below.Comprehensive evaluations indicate that in addition to providingimproved coverage indoors, with carefully designed resource managementsolutions that leverage spatial reuse, femto cells have a greatpotential to increase the system performance by two folds.

In contrast to UMTS architectures discussed above, exemplary embodimentsof the present invention provide more efficient interference mitigationby employing intelligent channel allocation using OFDMA features that donot necessarily require power control. Furthermore, unlike WLANsolutions, which require only good convergence properties for a single,mutually orthogonal channel, exemplary aspects of the present inventionadapt to varying availability of multiple OFDMA sub-channels, whichvaries with user-population, and possess both good throughput andconvergence properties. Moreover, contrary to MAC protocol allocation,distributed access does not permit application of ad-hoc networksolutions due to a lack of control messaging, preventing the directapplication of ad-hoc solutions. As discussed more further below,exemplary systems and methods may employ specialized random hashingtechnique adapted to enable an efficient medium access scheme for femtocells. In addition, because of the dependence of channel conditions onlocation, location is an important resource parameter that has not beenadequately considered by prior art methods. Current methods use locationinformation to determine the best wireless technology to operate on foroptimized performance. In contrast, exemplary implementations of thepresent invention include a novel location-based resource managementsolution for leveraging maximal spatial reuse from femto cells, asdiscussed herein below.

As noted above, exemplary methods and systems of the present inventionaddress the problem of a lack of direct coordination between macro andfemto cells and also between femto cells themselves. The femto cellsoperate on the same OFDMA technology as the macro cell and hence canreceive interference from macro cell transmissions. Although the rangeof a femto cell base station is typically much smaller than that of amacro cell base station, femto cell transmissions may cause interferenceto other femto cells and to macro cell users operating on the samechannel in close proximity to the femto cell. Thus, the efficiency ofresource management between macro and femto cells depends on the levelof indirect coordination that is enabled between the two. This, in turn,depends on whether (i) the femto cell base station belongs to the sameservice provider as the macro cell base station, which is referred toherein as the “coupled” resource management model; or (ii) the femtocell base station and the macro cell base station belong to differentservice providers, which is referred to herein as the “isolated”resource management model. For example, in the isolated case, the femtocell base station may belong to third party vendors to whom spectrum isleased out by the macro cell base station provider. Accordingly, bothmodels are considered below.

First, the isolated resource management model is considered. Asdiscussed further below, to avoid interference between the macro andfemto, one facet of exemplary systems and methods may optionally includeassignment of orthogonal resources, namely time and sub-channel slots,to macro and femto cell users. Exemplary systems and methods describedherein below indicate how available time and sub-channel resources in aframe may be split between the macro and femto users so as to maximizethe aggregate system utility, which may not only account for throughputbut also fairness. In addition, exemplary systems and methods may alsoadapt the split in resources over time to account for dynamic changes inmacro/femto cell user-population in a macro cell. Furthermore, exemplarysystems and methods may incorporate allocation of resources betweenfemto cell base stations, with a given split between macro and femtocell resources, in a completely distributed manner, without anycoordination, to leverage the potential spatial reuse available.

In the coupled model, exemplary systems may employ the same or similaraspects discussed above with regard to the isolated model in addition toother enhancing features. For example, according to other exemplaryaspects of the present invention, in addition to using orthogonalresources assigned to the femto cells via the resource split describedabove, femto cells may be dynamically allocated resources that wereoriginally assigned to the macro cell. For example, due to the smallersize of femto cells, those that are farther from the macro cell basestation will not be interfered by it. However, macro cell users close tosuch farther femto cell base stations might experience interference fromthe femto cell base stations. Hence, a farther femto cell base stationmay still reuse those macro cell resources that are not scheduled tomacro users lying in its vicinity. This permits for an additional levelof spatial reuse, where the femto cells reuse resources assigned to themacro cell. However, interference between macro and femto operating onthe same resources should be taken into account.

To facilitate this additional level of spatial reuse, some scheduleinformation of the macro cell base station should be provided to thefemto cell base stations. Thus, resource management in the coupled modelhas its own set of problems that are addressed by exemplaryimplementations of the present invention described below. For example,due to the distributed nature of resource allocation among femto cells,several frames are employed for convergence. This limits the timegranularity of macro schedule adaptation, resulting in a loss ofmulti-user diversity (MUD) and, consequently, a tradeoff between macroand femto user throughput. The tradeoff is also affected by thegranularity of macro schedule information provided. For example, while afiner granularity permits a higher potential for spatial reuse in femtocells, it also results in a higher overhead and a lower MUD for themacro cell. Moreover, while the farther femto cells can reuse macroresources, those closer to macro BS should be allocated orthogonalresources due to excessive interference. Hence, the resource splitshould take into account these factors in addition to considering alllevels of spatial reuse possible within femto calls.

Referring now in detail to the figures in which like numerals representthe same or similar elements and initially to FIG. 1, an exemplarysystem for implementing resource allocation methods disclosed herein inaccordance with the present invention is illustrated. As shown in FIG.1, system 100 may include a macro cell 102 servicing macro cell users130-138. In addition, system 100 may include a set of distributed femtocell base stations 104-112 servicing femto cell users or clients114-126. As shown in FIG. 1, each femto cell base station services femtocell clients in a corresponding femto cell 128.

The macro cell 101 is embedded with several femto cells 128 that mayrepresent residential or enterprise networks, as illustrated in FIG. 1.The femto base stations and the macro base station may operate using thesame OFDMA technology. For example, the femto base stations and themacro base stations may operate using WiMAX or LTE. The power used byeach femto BS may be an order of magnitude lesser than the macro cellbase station. This permits for multiple femto cell base stations tooperate in tandem on a given channel, thereby providing spatial reuse.Further, interference from the femto cell base stations to macro usersmay be restricted to its vicinity. In the exemplary system 100, there isno direct coordination between the femto cell base stations and themacro BS on the wireless edge. However, a network controller (not shown)on the backplane could potentially interface between the femto and macrobase stations on the cable backhaul. Given the delay on the backhaul,real-time coordination at the granularity of a frame may not be possiblebetween the macro and femto base stations.

In accordance with exemplary implementations of the present invention, atotal set of K users are considered, a fraction of which is distributeduniformly within the macro cell, while the remaining is distributeduniformly within the femto cells. The fraction of users within the macroand femto cells may vary with the load conditions. An OFDMA framestructure similar to WiMAX that is populated with time-frequencyresource tiles, as illustrated in FIG. 2, is considered. As shown inFIG. 2, an downlink (DL) OFDMA frame 202 and an uplink (UL) OFDMA frame204 includes tiles that have both time and frequency domain components.For example, the time slots 208 are distributed along the horizontalaxis while the sub-channel slots 210 are distributed along the verticalaxis.

An objective is to allocate resources, here formulated as tiles, tomacro and femto users such that aggregate system utility is maximized,whereby both throughput and fairness of the users is taken into account.The proportional fairness model may be employed, given its ability tostrike a good balance between utilization and fairness. The system canbe shown to converge to the optimum if the scheduler's decisions at eachepoch (interval) are made to maximize the aggregate marginal(incremental) utility, S_(max), wherein

-   -   S_(max)=arg max_(s) _(f) _(,S) _(m) {αΣ_(fεS) _(f)        ΔU_(f)+(1−α)Σ_(mεS) _(m) ΔU_(m)}. ΔU_(f),ΔU_(m) denote the        marginal utility received by femto and macro cell users f and m,        respectively, in a feasible femto/macro schedule (S_(f),S_(m)).

${\Delta \; U_{f}} = {{\frac{r_{f}}{{\overset{\_}{r}}_{f}}\mspace{14mu} {and}\mspace{14mu} \Delta \; U_{m}} = \frac{r_{m}}{{\overset{\_}{r}}_{m}}}$

for proportional fairness, where r _(f) and r _(m) represent averagethroughputs of the respective users, and α is the prioritization factor.As illustrated in FIG. 2, using the proportional fairness model,orthogonal sets of tiles 212 and 214 of an OFDMA frame 202 may beallocated for macro cell users and for femto cell users, respectfully.

Driven by different market models, as discussed above, isolated andcoupled models for resource allocation between macro and femto cells areconsidered. In the isolated model, resources are orthogonalized/splitbetween the macro and the femto cells to eliminate interference betweenthe two categories of users. The isolated model is suitable forscenarios where the femto BS that is bought by the consumer does notbelong to the same service provider as the macro BS. There may be acoarse level of indirect coordination between the femto cell basestation and the macro cell base station through the cable backhaul, withthe macro cell base station notifying the femto cell base stations ofthe manner in which resources are split. Alternatively, a networkcontroller may notify both the macro cell base station and the set ofdistributed femto cell base stations of the resource split.

The coupled model is a more sophisticated in that it enables indirectcoordination on the wireless link. Here, femto cells are permitted toreuse even parts of the macro resources, such as, for example, tileswithin set 212, without causing significant interference with macro cellbase station transmissions, thereby increasing the system capacity. Thecoupled model is suitable for scenarios where both the femto and macrobase stations belong to the same service provider, thereby enabling moreefficient resource utilization. For both the models, aslotted-synchronized system is considered, where the uplink and downlinkof femto cells is synchronized with that of the macro cell, as is thecase in current WiMAX femto cells standards. Exemplary systems andmethods discussed herein are applicable to both the UL and the DL.

A summary of how resources are allocated in both the isolated andcoupled models is illustrated in Table 1:

TABLE 1 Resources Isolated Model Coupled Model Macro Cell Used by macrocell users Shared by macro cell and exterior femto cell users Femto CellUsed by femto cell users Used by interior femto cell users

As discussed herein below, exterior femto cell base stations are femtocell base stations that experience interference from macro cell basestation transmissions that falls below some threshold. Conversely,interior femto cell base stations are femto cell base stations thatexperience interference from macro cell base station transmissions thatfalls above or at the threshold. The threshold is used to determinewhich femto cell base stations may reuse macro cell resources, asdiscussed below.

With reference now to FIG. 3, an exemplary method 300 for uncoordinatedallocation of tiles corresponding to time-frequency resource blocks ofOFDMA frames between distributed femto cell base stations in accordancewith one exemplary embodiment of the present invention is illustrated.Method 300 may be implemented in and performed by each given femto cell104-112 of system 100 to determine resource allocation in a distributedmanner. It should be understood that the tiles allocated to femto cellbase stations may be orthogonal tiles within set 214 designated forfemto cell base station use discussed above, tiles within set 212dedicated for a macro cell that may be reused by femto cells, or both.In addition, method 300 may be employed in both the isolated model andthe coupled model.

A random hashing technique for resource allocation among femto cells maybe utilized in method 300. A property of the technique is that itpermits for contention resolution without any coordination among thenodes involved. Let the set of nodes to be hashed belong to the universeU that can be represented by integers in [0,N−1] for some Ncorresponding to the total number of femto cell base stations contendingfor available tiles allocated to the macro cell. An objective is tohash/map the nodes from U into the set [0,n−1]. An exemplary hashingmechanism (h(u)) for a node u is shown in Algorithm 1 of Table 2:

TABLE 2 Algorithm 1-Hashing Mechanism: h(u) 1: Find a prime number p ≈ n. 2: Represent u in binary. Split it into blocks of └log₂ p┘ bits suchthat${u = \left\{ {u_{1},u_{2},\ldots \mspace{14mu},u_{r}} \right\}},{{{{where}\mspace{14mu} r} \approx {\left\lceil \frac{\log_{2}N}{\left\lfloor {\log_{2}p} \right\rfloor} \right\rceil \mspace{14mu} {and}\mspace{14mu} u_{i}}} \in {\left\lbrack {0,{p - 1}} \right\rbrack.}}$3: Generate a set A of all vectors of the form a = {a₁, a₂, . . . ,a_(r)}, where a_(i) ∈ [0, p − 1]. 4: Pick a vector a ∈ A at random. 5:Output h(u) = (Σ_(i=1) ^(r)a_(i)u_(i)) mod p

As illustrated in Algorithm 1 of Table 2, a prime number (P) close orclosest to the total number of femto cell base stations contending foravailable tiles allocated to the macro cell may be chosen. Thereafter,the node u may be represented in binary form, split into r blocks, wherer is

$r \approx {\left\lceil \frac{\log_{2}N}{\left\lfloor {\log_{2}p} \right\rfloor} \right\rceil.}$

A set of all vectors with P components is generated, one of which ischosen at random. The hash function h(u) is then determined to be themodulo-P sum of the dot product of the chosen vector and the noderepresentation.

It should be noted that the hash function h(u) using modulo-primebelongs to the class of universal hash functions. A property of thesefunctions is that for a hash aεA, the probability that two elements, u,vεU, map to the same hashed value, termed herein as “collision,” isbounded as

${P_{r}\left\lbrack {{h_{a}(u)} = {h_{a}(v)}} \right\rbrack} \leq {\frac{1}{n}.}$

To construct a distributed random access scheme from the hash function,how the nodes should hash into a given set of T time-frequency tilesshould be determined to find their allocations. In particular, the sizeof the hash table, t, to be used and the number of hash functions to beused, z, should be determined. To allocate tiles among the various femtocell base station nodes, a random access scheme may be employed in whichevery node splits the T tiles into consecutive blocks of t tiles eachand uses z hash functions inside each of these blocks to determine theirallocations. Thus, a node with a (t,z)-hashing scheme can obtain amaximum allocation of

$z \cdot \left\lceil \frac{T}{t} \right\rceil$

tiles. It can be shown with respect to (t*,z*) that t* corresponds tothe size of the contending node set and z*=1, provides the optimalhashing scheme.

TABLE 3 Algorithm 2 Distributed Random Access Scheme: DRA  1: F: Set offemto BS; id mapped to integer set [0, N)  2: % Distributed hashing inframe 1  3: if frame = 1 then  4:  for all f ∈ F do  5:   Initializeallocation: S_(f)(k) = 0, ∀k ∈ [1, T]  6:   Find the number ofinterfering femto BS: n  7:   Find a prime number, p ≈ n  8:   Pick arandom hash vector a ∈ A_(p), where a = {a₁, . . . , a_(r)},   ${{{where}\mspace{14mu} a_{i}} \in {\left\lbrack {0,{p - 1}} \right\rbrack \mspace{14mu} {and}\mspace{14mu} r}} = {\left\lceil \frac{\log_{2}N}{\left\lfloor {\log_{2}p} \right\rfloor} \right\rceil.}$ 9:   Write id of f(u) in binary; split it into r blocks such that   u ={u₁, . . . , u_(r)} 10:   ${{for}\mspace{14mu} i} = {1:{\left\lceil \frac{T}{p} \right\rceil \mspace{14mu} {do}}}$11:    x_(i) = {(i − 1)p + (Σ_(j=1) ^(r) a_(j)u_(j))mod p} 12:    S_(f)(x_(i) ) = 1 13:   end for 14:  end for 15:  Execute proportional fairschedule within S(f), ∀f i.e. ∀t ∈ S(f),  ${{Schedule}\mspace{14mu} h_{t}^{*}} = {\arg {\; \;}{\max_{h \in f}\frac{R_{h}(t)}{{\overset{\_}{R}}_{h}}}}$ Update R _(m) _(t) _(*) 16: else 17:  % Collision resolution in allsubsequent frames 18:  for all f ∈ F do 19:   Determine tiles thatexperienced collisions in S_(f); denote it by S_(f) ^(c) 20:   Pick arandom hash vector a ∈ A₂, where a = {a₁, . . . , a_(r)},   where a_(i)∈ [0, 1] and r = ┌log₂N┐. 21:   Write id of f(u) in binary; split itinto r blocks such that   u = {u_(i), . . . , u_(r)} 22:   for all k ∈S_(f) ^(c), do 23:    S_(f)(k) = (Σ_(j=l) ^(r)a_(j)u_(j))mod 2 24:   endfor 25:  end for 26:  Execute proportional fair schedule within S(f), ∀f27: end if

TABLE 4 Algorithm 3 DRA with Sensing Feature: DRA+  1: If frame = l then 2:  % Same procedure as in DRA  3: else  4:  % Access and contentionresolution in all subsequent frames  5:  for all f ∈ F do  6:  Determine idle tiles and colliding tiles in S_(f) ; denote it by S_(f)^(cs)  7:   Find the number of interfering femto BS: n  8:   Find aprime number, p ≈ n  9:   ${{for}\mspace{14mu} i} = {1:{\left\lceil \frac{T}{p} \right\rceil \mspace{14mu} {do}}}$10:    Find # elements (m) in S_(f) ^(cs) that belong to [(i − 1)p + 1,ip] 11:    Find the smallest prime number ≧m : q 12:    Pick a randomhash vector a ∈ A_(q), where a = {a₁, . . . , a_(r)},    ${{{where}\mspace{14mu} a_{i}} \in {\left\lbrack {0,{q - 1}} \right\rbrack \mspace{14mu} {and}\mspace{14mu} r}} = {\left\lceil \frac{\log_{2}N}{\left\lfloor {\log_{2}q} \right\rfloor} \right\rceil.}$13:    Write id of f(u) in binary; split it into r blocks such that    u= {u_(i), . . . , u_(r)} 14:    y_(i) = (Σ_(j=1) ^(r)a_(j)u_(j)) mod q15:    x_(i) =S_(f) ^(cs)(y_(i)) % Find the corresponding tile index 16:   S_(f)(x_(i)) = 1 17:   end for 18:  end for 19:  Execute proportionalfair schedule within S(f), ∀f 20: end if

Returning now to FIG. 3 and referring to Tables 3 and 4, method 300 maybe delineated by two phases: a) hashing-based allocation in a firstframe, which may be performed in steps 304-310; and b) collisionresolution, which may be performed in steps 312 and 310. Alternativecollision resolution embodiments are illustrated in Tables 3 and 4, andare discussed more fully below. While alternate embodiments of collisionresolution are described, the hashing based allocation may be employedwith both types of collision resolution processes. In addition, itshould be understood that all processes described with respect to method300 may be applied in both the isolated model and the coupled modeldiscussed above. Method 300 may be performed for each time frame orOFDMA frame and repeated over time for every frame.

Method 300 may begin at step 302 in which a given femto cell basestation determines whether the current frame is a first frame of a setof L frames. The set of L frames may correspond to a number of frameswithin which a femto cell base station allocation process converges to aset of allocated tiles for its users or clients that do not collide withtiles employed by femto cell base stations within an interference range.L may correspond to the average minimum number of frames in whichresource allocation converges or may include some tolerance to ensurethat optimal convergence occurs at each femto cell base station (BS).

If the current frame is determined to be a first frame in the L set offrames, then the method may proceed to step 304 in which a prime number(P or p) may be selected based on a total number (N or n) of femto cellbase stations contending for tiles. For example, as illustrated in steps4-7 of Algorithm 2 in Table 3, an allocation may be initialized,S_(f)(k)=0, ∀kε[1,T], where S_(f)(k)=0 denotes that a tile k is notallocated to the given femto cell base station and S_(f)(k)=1 denotesthat a tile k is allocated to the given femto cell base station.Further, a femto cell base station may find the number of femto cellbase stations contending for tiles. The number of contending basestations may be provided by a network controller or by other means priorto instituting the allocation method. In addition, the prime number Pselected may be a prime number close to or closest to the total numberof contending femto cell base stations. It should be understood that thenumber of contending femto cell base stations may depend on the set ofinterfering femto BS or the size of the largest maximal clique in aninterference graph formed between femto cell base stations.

At step 306, the given femto cell BS may generate a hash function byrandomly choosing a hash vector with components selected from a table ofsize P and may divide the available tiles into sets of P tiles each. Forexample, as illustrated in step 8 of Algorithm 2 in Table 3, the femtocell BS selects a random hash vector aεA_(p), where a={a₁, . . . ,a_(r)}, a_(i)ε[0,p−1] and

${r = {\left\lceil \frac{\log_{2}N}{\left\lfloor {\log_{2}p} \right\rfloor} \right\rceil.}}\;$

Further, the given femto cell BS represents an identifier of the givenfemto cell base station f(u), which is an identifier of the base stationmapped to the integer set [0, N), in binary form and splits the binaryrepresentation into r blocks to generate an identifier vector u suchthat u={u₁, . . . , u_(r)}, as shown in step 9 of Algorithm 2 in Table3. Thereafter, the given femto cell BS splits the available tiles intoblocks having P tiles each, as shown in step 10 of Algorithm 2 in Table3 to permit application of the hash function on the blocks. As shown instep 11 of Algorithm 2 in Table 3, the hash function in this exemplaryembodiment is {(i−1)p+(Σ_(j=1) ^(r)a_(j)u_(j))mod p}, where i is anidentifier for each block of P tiles.

At step 308, the femto cell base station may allocate itself tiles fromthe sets of P tiles by applying the hash function to each set. Forexample, as shown in blocks 11 and 12 of Algorithm 2 in Table 3, thefemto cell base station may randomly choose tiles, x_(i), by applyingthe hash function to each value of i representing each block of P tiles,x_(i)={(i−1)p+(Σ_(j=1) ^(r)a_(j)u_(j))mod p}. The chosen tiles areallocated to the given femto cell base station, S_(f)(x_(i))=1.

At step 310, the femto cell BS may assign the allocated tiles to itsclients and may transmit assignment messages to the clients to permitcommunication on the allocated tiles. For example, as shown in step 15of Algorithm 2 in Table 3, the femto cell base station may execute aproportional fair schedule within the set of allocated tiles, S(f),∀fi.e. ∀tεS(f). For example, for each tile t, each user h is evaluated todetermine the user, h_(t)*, having the maximum utility for theparticular tile t:

${h_{t}^{*} = {{argmax}_{h \in f}\frac{R_{h}(t)}{{\overset{\_}{R}}_{h}}}},$

where R_(h)(t) is the instantaneous rate and R _(h) is the averagethroughput for a user or client h.

Returning to step 302, if the current frame is a frame within the set ofL frames that is subsequent to the first frame, the method may proceedto step 312, in which the given femto cell base station performscollision resolution for the current frame. As mentioned above,alternative, exemplary embodiments for collision resolution aredescribed herein.

With reference to FIG. 4 and continuing reference to Table 3 and FIG. 3,one exemplary collision resolution embodiment is described and may beperformed at step 312. Here, the given femto cell BS retains theallocation for the successful tiles but attempts to resolve contentionin the colliding tiles in the subsequent frames. The collisionresolution may begin at step 402, in which the femto cell base stationdetermines which tiles allocated to itself in the previous frame collidewith signals transmitted by other femto cell base stations. For example,the given femto cell base station may monitor the transmission ofsignals on allocated tiles and determine whether any interferencereceived on the allocated tiles exceeds some threshold to find thecolliding tiles. Alternatively, the given femto cell base station mayreceive collision information measured by and transmitted from one ormore clients. The colliding tiles are denoted here as S_(f) ^(c).

At step 404, the given femto cell base station may generate a secondhash function by randomly choosing a second hash vector with componentsselected from a hash table of size two. For example, as shown in step 20of Algorithm 2 in Table 3, a random hash vector aεA₂ is selected,wherein a={a₁, . . . , a_(r)}, a_(i)ε[0,1] and r=┌ log₂N┐. Thus, thefemto cell base station in this exemplary embodiment uses a hash tableof size two to determine if it should continue accessing the collidedtiles or cease using the collided tiles in subsequent frames. Similar tothe processing described with respect to the first frame, the femto cellbase station represents the identifier of the given femto cell basestation f(u), taken from the integer set [0,N), in binary form andsplits the binary representation into r blocks to generate a secondidentifier vector u such that u={u₁, . . . , u_(r)}, as shown in step 21of Algorithm 2 in Table 3. However, r here is ┐ log₂ N└, as statedabove. As shown in step 23 of Algorithm 2 in Table 3, the hash functioncomprises performing modulo-2 addition on the dot product of the secondhash vector a and the second identifier vector u.

At step 406, the given femto cell BS may reallocate tiles from thecolliding tiles for its use by applying the second hash function to eachof the colliding tiles. For example, as shown in steps 22 and 23 in theexemplary embodiment described in Algorithm 2 of Table 3, for each tilekεS_(f) ^(c), the femto cell base station randomly selects tiles withinS_(f) ^(c) by applying the second hash function: S_(f)(k)=(Σ_(j=1)^(r)a_(j)u_(j))mod 2. As noted above, the given femto cell base stationmay retain tiles, for its use, that do not collide with signalstransmitted by other femto cell base stations. Thereafter, the processmay proceed to step 310 in which the given femto cell base stationassigns the reallocated tiles and the retained tiles to its clients andtransmits assignment messages to its clients to permit communication onthe reallocated and the retained tiles. Steps 312 and 310 may beiterated for subsequent frames until all tiles assigned by the givenfemto cell to its clients do not collide with signals transmitted byother femto cell base station. For example, during each iteration, atstep 314, it may be determined whether tiles assigned by the given femtocell base station to its clients collide with signals transmitted byother femto cell base station. If there are colliding tiles, steps 310and 312 iterated; otherwise the method may end. Once collision has beenresolved, the schedule for the allocated tile is retained in thesubsequent frames.

It should be noted that the hash vector for every hashing period can berandomly chosen by each femto cell BS using seeds that are consistentacross femto cell base stations or the hash vector for every hashingperiod can be assigned by the macro cell base station or a networkcontroller. Further, it can be shown that Algorithm 2 of Table 3, whichmay be referred to as a Distributed Random Access (DRA) scheme,converges to a stationary allocation within 3-4 frames. The allocatedframes obtained after conversion may be retained until it is determinedthat circumstances warrant a reallocation of resources between macro andfemto cell base stations. For example, such circumstances may include anappreciable change in the user population in femto cells and/or themacro cell upon which resources allocated to femto cells may also bevaried, as discussed in more detail below. It should also be noted thatthe average case performance of the DRA the allocation scheme is boundedwithin

${\frac{1}{e} \leq \frac{C_{DRA}}{C_{OPT}} \leq \frac{2}{3}},$

where C_(DRA) is the total throughput over a set of contending nodes perframe for the DRA scheme and C_(OPT) is the total throughput over a setof contending nodes per frame for an optimal scheme, respectively. Inthe optimal scheme, all T available tiles are allocated. Returning nowto step 304 of method 300, as noted above, the determination of thenumber of femto cell base stations contending for available tiles maydepend on the set of interfering femto BS or the size of the largestmaximal clique in an interference graph formed between femto cell basestations. In the particular exemplary embodiment described in Algorithm2 of Table 3, all nodes are assumed to have the same number ofcontending neighbors. However, in a practical network deployment, thedensity of femto BS varies from region to region, inducing variations inthe degree of contention seen by adjacent femto BS. In Algorithm 2 ofTable 3, each femto cell BS individually measures its interfering degree(d_(i)) and uses it for deciding the size of the hash table, which keepsthe algorithm simple and completely distributed. However, in thepresence of heterogeneous BS density, the approach forgoes someperformance due to over-estimated contention; the size of the largestmaximal clique (d_(c)) including the femto cell BS provides a bettermeasure for the size of the hash table employed. A maximal cliquecomprises the largest set of mutually interfering femto cell basestations and its determination may be made with assistance from anetwork controller or the macro cell base station in alternativeembodiments. In the presence of homogeneous BS density d_(c)=d_(i) butwith heterogeneous density d_(c)≦d_(i). The larger the gap between d_(c)and d_(i), larger is the loss in performance in the interference-degreebased approach. While using the interference degree forgoes someperformance, it can be shown that the performance loss is marginal forpractical femto BS densities, where the gap between d_(c) and d_(i) isnot significant for most femto cell base stations. This, coupled withits completely distributed nature, makes the interference-degreeapproach an attractive means for determining the number of contendingfemto cell base stations for DRA.

As noted above, in DRA, once hashing is completed in the first frame todetermine channel access, re-hashing may be performed only on thecollided tiles to resolve contention in the subsequent frames. When afemto BS experiences a collision in a tile, multiple colliding femtobase stations hash into the same tile, which in turn leaves aproportional number of tiles in the hashing period idle. However, theDRA scheme mentioned above does not use such idle tiles for rehashing insubsequent frames, thereby accounting for the saturating performance ofDRA with respect to an optimal allocation scheme irrespective of whetherinterference degree or maximal clique size is used for the hash tablesize. While determining collision is relatively straight-forward, asdiscussed above, sensing an idle tile involves evaluating all availabletiles. For example, a given femto cell base station may monitor thetransmission of signals on all tiles and determine whether anyinterference received on any of the tiles is below some threshold tofind the idle tiles. Alternatively, the given femto cell base stationmay receive idle tile information measured by and transmitted from oneor more clients. With an idle tile detection feature, the performancegap between the DRA approach and the optical approach can be bridged.

Further, while employing d_(c) permits a more efficient spatial thanemploying d_(i), d_(c) is still a parameter local to the given femtocell BS, determined with the help of a network controller or the macrocell BS, that is dependent on which allocation decisions are made. Theheterogeneity in femto BS density introduces another level of spatialreuse to leverage using two-hop information around the femto BS.Specifically, to leverage this additional level of spatial reuse, everyfemto cell BS should not only determine its largest maximal clique sizebut also those of its interfering femto BS.

For example, with reference to FIG. 5, an interference topology 516 forfemto cell base stations a 506, b 508, c 512, d 514 and e 510. Inaddition, tables 502 and 504 illustrate determination of the number offemto cells contending for available tiles using maximal clique size andinterference degree, respectfully. For example, for femto cell b 508,use of maximal clique size yields 3 femto cell base stations contendingfor available tiles while use of interference degree yields 4 femto cellbase stations contending for available tiles. The tables also indicatewhich tiles, numbered 1-12, are allocated to each femto cell basestation. For example, femto cell base station a 506 is allocated tiles1, 4, 7 and 10 in both tables. Elements 518 and 520 indicate tiles thatare unused due to a lack of 2-hop information and tiles that are unuseddue to an over-estimated contention, respectively. BS d 514 has room forallocations in tiles 2 and 8 in the d_(c) approach. Because d 514'sinterfering neighbor c 512 has a higher maximal clique size, d 514 canbe more aggressive, as c will contend for a lesser number of tiles.Thus, each femto cell BS may employ the largest maximal clique size ofits interfering neighbor base stations in addition to its own toleverage spatial reuse. However, by using an idle tiles sensing feature,such spatial reuse is automatically leveraged as and when idle tiles aredetected. Thus, while the d_(c) approach performs better than the d_(i)approach with collision detection only, both approaches perform close tooptimal with fast convergence if the femto cells employ both collisionand idle tile detection. Although the sensing feature enables the d_(i)to improve spatial reuse, the d_(c) approach has better fairness orutility than the d_(i) approach, which has a bias against femto cellbase stations with a larger interference degree (d_(i)>d_(c)). However,it should be understood that exemplary embodiments of the presentinvention may employ one or both of the d_(i) approach and the d_(c)approach.

With reference now to FIG. 6, with continuing reference to FIGS. 3 and4, Table 3 and Table 4, another exemplary method 600 for resolvingcollisions taking into account idle tiles that may implement step 312 ofmethod 300 and that may be performed in lieu of method 400 isillustrated. Table 4 provides an exemplary algorithm, denoted “DRA+” orAlgorithm 3, for allocating tiles using a collision resolution schemethat employs an idle tile sensing feature. Method 600 may begin at step602, after a determination in step 302 that the current frame is not afirst frame. At step 602, the given femto cell base station may compilea group of tiles composed of idle tiles and colliding tiles from theprevious frame. Here, the colliding tiles may be selected from the setof tiles allocated to the given femto cell base station in the previousframe. The colliding tiles may be determined in the same mannerdiscussed above with respect to step 402. In addition, the idle tilesmay, for example, be detected by receiving idle tile information fromthe client or by determining whether any interference received on any ofthe tiles is below some threshold, as discussed above. The group of idleand colliding tiles are denoted here as S_(f) ^(cs).

It should be noted that the following steps, steps 606-610, may beperformed for each set of the sets of P tiles that includes any of theallocated idle and colliding tiles. The sets of P tiles may correspondto those determined in step 306, for example. Thus, at step 604, thegiven femto cell BS may determine whether all such sets that includeidle and colliding tiles have been evaluated. If not all of such setshave been evaluated, steps 606-610 may be performed on the next set of Ptiles. As shown in step 9 of the DRA+ algorithm in Table 4, evaluationof each block of P tiles may be implemented in a “for” loop.

At step 606, for the set of P tiles currently evaluated, the given femtocell BS may determine the number of tiles from the group of idle andcolliding tiles that are within the corresponding set of P tiles. Forexample, as illustrated in step 10 of the DRA+ algorithm of Table 4, thegiven femto cell BS determines how many (m) of the idle and collidingtiles are in the corresponding block of P tiles currently evaluated.

At step 608, the given femto cell BS may generate a second hash functionby randomly choosing a second hash vector with components selected froma hash table of size q, wherein q is a lowest prime number that isgreater than or equal to m. For example, as illustrated in step 12 ofthe DRA+ algorithm of Table 4, the femto cell BS selects a random hashvector aεA_(q), wherein a={a₁, . . . , a_(r)}, a_(i)ε[0,q−1] and

$r = {\left\lceil \frac{\log_{2}N}{\left\lfloor {\log_{2}q} \right\rfloor} \right\rceil.}$

In addition, as shown in step 13 of the exemplary DRA+ algorithm ofTable 4, the identifier of the given femto cell base station, discussedabove with respect to method 300, may be represented in binary from andsplit into r blocks to generate a second identifier vector u={u₁, . . ., u_(r)}. In addition, the hash function may comprise performingmodulo-q addition on the dot product of the second hash vector and thesecond identifier vector, (Σ_(j=1) ^(r)a_(j)u_(j))mod q, as shown instep 14 of the DRA+ algorithm in Table 4.

At step 610, the femto cell BS may reallocate tiles from the group ofidle and colliding tiles to itself by applying the second hash functionto the corresponding set. For example, as shown in steps 14-16 in theexemplary DRA+ algorithm of Table 4, the given femto cell base stationallocates a tile by selecting one of the idle or colliding tiles in thecorresponding block using the second hash function. It should be notedthat the term “reallocate,” with respect to the group of idle andcolliding tiles, should be construed to mean that colliding and/or idletiles are allocated to the given femto cell BS. It should also beunderstood that any tiles allocated to the given femto cell base stationin a previous frame that do not collide with transmissions from otherfemto cell base stations and are not idle may be retained by the givenfemto cell base station for its use. The method may return to step 604and steps 606-610 may be repeated until all sets or blocks of tiles havebeen evaluated.

If, at step 604, the last set of the sets of P tiles is reached, theprocess may proceed to step 310 in which the given femto cell basestation assigns the reallocated tiles and the retained tiles to itsclients and transmits assignment messages to its clients to permitcommunication on the reallocated and the retained tiles. As discussedabove, steps 312 and 310 may be iterated for subsequent frames until alltiles assigned by the given femto cell to its clients do not collidewith signals transmitted by other femto cell base stations. Oncecollision has been resolved, the schedule for the allocated tile isretained in the subsequent frames.

It should be understood that if, at any point in method 600, no idletiles are detected, the process may revert to method 400 in which a hashtable of size two is employed to generate a hash function and apply thehash function to each collided tile, as discussed above with respect toFIG. 4.

Unlike collision resolution method 400, collision resolution method 600determines tiles that are in collision as well as those that are idleand rehashes based on the remaining free resources. Hence, the size ofthe hash table varies from one frame to another and is not fixed at two.Thus, by the end of method 600, all resources are used, thereby bridgingthe performance gap with respect to the optimal allocation scheme. Itcan be shown that the convergence to optimal is on average, performedwithin 5-6 frames, which well within limits of practical feasibility.

As discussed above, in both the isolated and coupled models, femto cellbase stations may employ orthogonal resources, such as those representedby element 214 in FIG. 2. In the isolated model, resources may beorthogonalized between the macro and femto cells to reduce compatibilityand coordination between them so that third-party femto BS can bepurchased by consumers. In addition, in the coupled model, orthogonalresources may be used by femto cell base stations in areas in which theymay interfere with macro cell base station transmissions, as discussedin more detail below with respect to location-based resource allocationmethods. In either case, the macro cell BS or the network controller maymaximize the aggregate system utility by basing the split of resources,for example, tiles in a frame, between macro and femto cell basestations on user population for one of or both the femto cell basestations and the macro cell base station. User-population may varybetween macro and femto cell users due to users moving in and out ofbuildings, for example. Further, user-population variance may also bedue to users closing connections with the macro cell base station and/orfemto cell base stations and new users accessing either type of basestation.

While indirect coordination can be enabled between the femto cell basestations and the macro cell base station on a cable backhaul, suchcoordination typically can be enabled only at coarse time scales, on theorder of several tens-hundreds of frames, and not at the granularity ofevery frame. When the user population changes appreciably for macro cellbase stations and/or femto cell base stations, one potential approachfor dynamically allocating resources may be to start from the currentresource split and iteratively adapt (adjust) the split until an optimumallocation is attained. However, this would incur a large number ofiterations, during which time a sub-optimal allocation would be used,thereby depreciating system performance.

To enable faster convergence to the optimal resource split, anintelligent initial split should be set before performing the dynamicadaptation. The optimal split for an idealistic model is computed inclosed-form. Using this as the starting point, the split may beiteratively adapted to converge to the optimum in practice. The highquality of the initial starting point helps the adaptation quicklyconverge within four iterations. For the isolated case, it can be shownthat if all the femto/macro cell users receive the same throughput in anidealistic model, then the optimal number of tiles allocated to thefemto cells is

$\frac{NF}{F + M},$

where N is the total number of tiles, and F and M are the number offemto cell users and macro cell users in the system, respectively. Inaddition, for the coupled case, it can also be shown that if all thefemto/macro cell users receive the same throughput in an idealisticmodel, then the optimal number of tiles allocated to the femto cells is

$\frac{{NF}_{i}}{F + M},$

where N is the total number of tiles, and F and M are the number offemto cell users and total users in the system respectively, with F_(i)being the number of interior femto cell users. Further, in the coupledcase, it can be shown that it is optimal for only the interior femtocell users to use the orthogonal channels allocated to the femto cellbase stations, while the exterior femto users only reuse the tilesallocated to the macro cell base station. However, exterior femto cellusers may also use the orthogonal channels allocated to the femto cellsin addition to reusing tiles allocated to the macro cell base station inexemplary embodiments of the present invention.

It should be noted that “interior femto cells” should be understood tomean femto cells that are interfered by transmissions on macro cellresources. Conversely, “exterior femto cells” cause interference totransmissions on macro cell resources. Determination of whether a femtocell base station is interior or exterior may depend on its location andmay be based on a degree of interference that exceeds some threshold, asdiscussed further below, which may be selected in accordance with designchoice.

Given a practical geographical distribution of macro and femto users,the throughputs provided by a proportional fairness model would varyeven within users of the same category (macro or femto). To addressthis, further adaptation of the split may be applied, as describedfurther below.

TABLE 5 Algorithm 4 Femto-Macro Allocation: FMA1  1: % G (Macro BS orn/w controller) estimates change in femto, macro user population  2: if|F_(c) − F_(l)| + |M_(c) − M_(l)| ≧ β(F_(l) + M_(l)) then  3:  ${T_{f} = \left\lfloor \frac{{NF}_{c}}{F_{c} + M_{c}} \right\rfloor},{T_{m} = {T - T_{f}}}$ 4:  U_(c) = Execute MA_DRA(T_(m), T_(f))  5:  U_(l) = U_(c)  6:  T_(f)⁺ =T_(f) + δ, T_(m) = T − T_(f)  7:  U⁺ = Execute MA_DRA(T_(m), T_(f) ⁺) 8:  T_(f) ⁻ = T_(f) − δ, T_(m) = T − T_(f)  9:  U⁻ = ExecuteMA_DRA(T_(m), T_(f) ⁻) 10:  if U⁺ > U_(l) then 11:   Δ = +δ; T_(f) =T_(f) ⁺; U_(c) = U⁺ 12:  else 13:   if U⁻ > U_(l) then 14:    Δ = −δ;T_(f) = T_(f) ⁻; U_(c) = U⁻ 15:   end if 16:  else 17:   Δ = 0 18:  endif 19:  while U_(c) ≧ γU_(l) do 20:   U_(l) = U_(c) 21:   T_(f) =T_(f) + Δ, T_(m) = T − T_(f) 22:   U_(c) = MA_DRA(T_(m), T_(f)) 23:  endwhile 24:  Retain (T_(m), T_(f)); F_(l) = F_(c), M_(l) = M_(c) 25: else26:  Continue with current split 27: end if 28: 29: MA_DRA(T_(m), T_(f))30: Execute MA(T_(m)) for L frames 31: Execute DRA(T_(f)) for L frames32: G collects utility information from all femto and macro BS andoutputs: U = αU_(F) + (1− α)U_(M)

With reference now to FIG. 7, a method 700 for dynamic allocation oftiles corresponding to time-frequency resource blocks of OFDMA framesbetween a macro cell base station and a set of distributed femto cellbase stations within the macro cell based on user-population variance inaccordance with one exemplary embodiment of the present invention isillustrated. Method 700 may be applied in the isolated architecture offemto and macro cell base stations. To aid in describing method 700,reference is also made to Table 5 provided above, which illustrates anexemplary algorithm, termed “FMA1” or “Algorithm 4,” for dynamicallyallocating tiles of OFDMA frame based on user-population in the isolatedcase.

Method 700 may begin at step 702, in which the macro cell base stationor the network controller determines the change in macro cell and/orfemto cell user population. The change may be determined based oninformation provided by the macro cell base station and/or informationprovided by femto cell base stations through a cable backhaul, forexample.

At step 704, the macro cell base station or a network controller detectsthat a change in user-population serviced by at least one of the macrocell base station or the set of distributed femto cell base stationsexceeds a threshold. For example, as shown in step 2 of the exemplaryAlgorithm 4 of Table 5, user-population variance may exceed a threshold:|F_(c)−F_(l)|+|M_(c)−M_(l)|≧β(F_(l)+M_(l)), where F_(c) is the currentnumber of femto cell users, F_(l) is a previous number of previous femtocell users, M_(c) is the current number of macro cell users, M_(l) is aprevious number of previous femto cell users, and β is a constant thatmay be based on design choice. If the user-population variance does notexceed the threshold, then the current allocation scheme is retained andresource allocation can be continued using the current split. If theuser-population variance does exceed the threshold, the method mayproceed to step 706. While the determination of user-population varianceby considering the change in both femto and macro user populations isemployed in exemplary Algorithm 4, the determination of user-populationvariance may alternatively be based on either femto cell users or macrocell users.

At step 706, the macro cell base station or the network controller mayperform an initial allocation in which the set of femto cell basestations are allocated

$\frac{NF}{F + M}$

of the available tiles and in which the macro cell base station isallocated remaining tiles. Here, N is the total number of said tiles, Fis the total number of femto cell users and M is the total number ofmacro cell users. As shown in Algorithm 4 of Table 5, the initial tileallocation to the femto cell base stations may be denoted as T_(f), theinitial tile allocation to the macro cell base stations may be denotedas T_(m), and the total number of available tiles may be denoted as T.

At step 708, the macro cell base station or the network controller mayreceive utility information from the femto cell base stations and, if anetwork controller is employed, the macro cell base station. Forexample, as shown in steps 4-5 of Algorithm 4 of Table 5, macro andfemto cell resource allocation mechanisms may be executed. For the macrocell access resource allocation mechanism (MA), in this exemplaryembodiment, the macro cell base station employs proportional fairnessbased resource allocation, whereby for every tile (t) assigned to themacro BS, the macro user providing the highest marginal utility isscheduled as

${m^{*}(t)} = {{argmax}_{m}\frac{R_{m}(t)}{{\overset{\_}{R}}_{m}}}$

where R_(m)(t) is the rate of user m on tile t, while R _(m) is theaverage throughput of user m thus far. For femto resource allocation,one of the DRA/DRA+ schemes described above with respect to method 300is individually run by the femto BS. The femto cell resource allocationmay be run for L frames to permit convergence in the femto case. Afterthe resource allocation phase ends, the network controller may receiveutility information from the femto cell base stations, for example, froma cable backhaul, and from the macro cell base station to determine theaggregate system utility resulting from the current resource split. Forexample, as discussed above, the aggregate system utility isU=αU_(F)+(1−α)U_(M), where U_(F) is the utility received by femto cellusers, U_(M) is the utility received by macro cell users, and α is theprioritization factor.

At step 710, the macro cell or the network controller may vary thenumber of tiles allocated to the set of femto cell base stations. Forexample, as shown in steps 6-18 of Algorithm 4 of FIG. 5, the networkcontroller or the macro cell base station increases and decreases thefemto resources (fraction of tiles allocated to femto cells) by δ andobserves the resulting utility to determine the direction of adaptationaffording a higher aggregate utility.

At step 712, the macro cell base station or the network controller mayiterate the variance until a utility measure for the macro and femtocell base stations is optimized. For example, once the direction ofadaptation is determined, as shown in steps 19-24 of Algorithm 4 ofTable 5, the process iterates to adjust the varying in the determineddirection of adaptation and to converge to the optimal resource split,which is retained thereafter until the next appreciable user populationchange.

It can be shown that by using a carefully chosen starting point asdiscussed above permits resource allocation between the macro cell basestation and the femto cell base stations to converge to the optimumwithin four iterations with each iteration involving L=5 frames, therebyresulting in a total of about 20 frames. Given that user populationvariations occur at the granularity of several minutes, the convergencerate of FMA1 (20 frames×5 ms/frame=100 ms) is significantly fast,keeping the performance loss due to sub-optimality during the adaptationphase to a minimum.

With reference now to FIG. 8, a method 800 for dynamic allocation oftiles corresponding to time-frequency resource blocks of OFDMA framesbetween a macro cell base station and a set of distributed femto cellbase stations within the macro cell based on user-population variance inaccordance with one exemplary embodiment of the present invention isillustrated. Method 800 may be applied in the coupled architecture offemto and macro cell base stations. Method 800 is the same as method 700except that the femto cell base stations are initially allocated

$\frac{{NF}_{i}}{F + M}$

in step 806 as opposed to

$\frac{NF}{F + M}$

in step 706, where N is the total number of tiles, and F and M are thenumber of femto cell users and total users in the system respectively,with F_(i) being the number of interior femto cell users. F_(i) may bedetermined locally by each femto cell base station and provided to thenetwork controller or the macro cell base station. In addition, theresource allocation between macro and femto cells may be replaced withthe location-based resource allocation scheme, described in detailbelow. With the reuse of macro resources by femto cells, the samesolution proposed in the isolated model does not provide goodconvergence. Thus, the starting point of adaptation has been modified toaccount for the additional level of spatial reuse and to ensure fastconvergence.

While architectures described above permit for spatial reuse within thefemto cells, strict orthogonalization of resources between macro andfemto cells tends to restrict system capacity. To enlarge systemcapacity, certain femto cells may be configured to reuse resources, suchas resources 212 described above with respect to FIG. 2, which have beenallocated to the macro cell. For example, due to the smaller size offemto cells, those that are farther, such as femto cell BS 104 of FIG.1, from the macro cell BS will not be interfered by it. But, macrousers, such as macro user 130, close to such farther femto cell basestations would still be interfered by the farther femto cell BS.However, the farther femto BS, for example, femto cell BS 104, can stillreuse those macro resources that are not scheduled to macro users, suchas macro cell user 130, located in its vicinity. This permits for anadditional level of spatial reuse where the femto cells can reuseresources assigned to the macro cell. However, the interference betweenmacro and femto cells operating on the same resources should now betaken into account.

Because the interference on macro users is macro-schedule dependent, tofacilitate this additional level of spatial reuse, some scheduleinformation of the macro cell should be provided to the femto cells.Providing this information on the backhaul would incur significantdelay, reducing the usefulness of such information for resourceallocation in femto cells. Hence, in accordance with exemplary aspectsof the present invention, the forward wireless link of the macro cellsmay be used to convey such information. In WiMAX systems, for example,the macro cell BS computes the schedule for the uplink and downlinkframes and appends it to the beginning of the respective frame in theform of a resource allocation map (MAP), indicating to the user thetiles allocated to it. Further, this MAP is sent at the lowestmodulation-coding rate to permit for reliable decoding at the user orclient devices. However, to serve the purpose of permitting additionalspatial reuse in the femto cell base stations, in addition to the userallocation to a tile, zone allocation information is also appended tothe tile, indicating to the femto cell BS the location (zone) where thespecific tile will be used in the macro cell. The computation of thezone itself is explained in detail below.

The femto cell base stations will attempt to decode the MAP sent by themacro BS to determine the geographical usage of resources in the macrocell. This helps them determine the set of tiles (resources) that can bereused without incurring interference from the macro cell BS or causinginterference to macro users in their vicinty. Given the distributednature of random access, several frames (L) are employed forconvergence, where the benefits of additional spatial reuse can berealized in femto cells. However, this implies that the macro scheduleinformation, being leveraged by femto cells, should remain unchanged forL frames from the point of view of the femto cells. If conventionaluser-based scheduling is employed, retaining the same schedule overseveral frames would result in loss of performance due to reduced MUDarising from varying channel conditions of users across frames. Hence,the larger the number of frames over which the macro schedule isretained, the higher is the femto user throughput due to convergence,but lower is the macro user throughput. Thus, to permit efficient reuseof macro resources, the trade off between femto and macro userthroughput that arises from conventional user-based macro schedulingshould be addressed. Further, to maximize aggregate system utility inthe presence of additional spatial reuse, the granularity of locationinformation that is provided with the macro schedule as well as themanner in which resources between macro and femto cells are allocatedand adapted should be assessed.

To address the tradeoff in femto and macro user throughput arising fromuser-based scheduling, the following location-based resource allocationsolution (LRA) for macro users may be employed. Instead of assigning atile to simply the user providing the highest marginal utility, LRAdetermines the user based on not only its highest marginal utility butalso the occupancy of the zone to which it belongs in the first frame

$\begin{matrix}{{{m^{*}(t)} = {{argmax}\left\{ {\frac{R_{m}(t)}{{\overset{\_}{R}}_{m}}{M_{z{(m)}}}} \right\}}}{{z^{*}(t)} = {z\left( {m^{*}(t)} \right)}}} & (2)\end{matrix}$

where z(m) denotes the zone to which user m belongs and M_(z(m))indicates the set of macro users in zone z(m) with |M_(z(m))| being thesize of the set. A key observation that will be leveraged is that theinformation employed by the femto cell BS to reuse macro resources isnot the specific macro user assigned to a tile, but the zone in whichthe assigned macro user will use the tile to determine the potentialinterference it could cause and hence avoid it. Thus, it is sufficientif the zone allocations of the tiles are alone retained for L frames forthe femto BS to adapt and efficiently reuse the macro resources. Thisgives LRA the flexibility to switch users that are within the same zonein subsequent frames based on varying channel conditions without havingto change the zone information of the schedule that was notified to thefemto cell BS. This alleviates the loss in MUD and hence macro userthroughput in subsequent frames. Thus, macro cell BS schedules users insubsequent frames as follows,

${m^{*}(t)} = {\arg \; {\max\limits_{m \in {z^{*}{(t)}}}\left\{ \frac{R_{m}(t)}{{\overset{\_}{R}}_{m}} \right\}}}$

It should be noted that, choosing densely populated macro zones willretain maximum MUD gains by increasing the probability of finding a userwith good channel gain within the zone. However, it will not be able toensure fairness, especially in the sparsely populated zones. Hence, aproper balance of the marginal utility of users as well as the zone sizeshould be utilized to address the tradeoff effectively as in equation 2.One detailed, exemplary implementation of LRA is provided in Algorithm 5of Table 6:

TABLE 6 Algorithm 5 Location-based Resource Allocation, LRA, example  1:

 ← set of femto BS receiving strong interference from macro BS;

 (f) ← {set of T_(f) tiles}, ∀_(f) ∈ F  2: for Every period of L framesdo  3:  % Macro BS Operations  4:  if first_frame then  5:   for all t ∈[1, T] do  6:    ${m^{*}(t)} = {\arg \mspace{11mu} {\max_{m}\left\{ {\frac{R_{m}(t)}{{\overset{\_}{R}}_{m}}{M_{z{(m)}}}} \right\}}}$ 7:    z^(*)(t) = z(m^(*)(t))  8:    Determine zone information, Z =∪_(t)z^(*)(t)  9:   end for 10:  else 11:   for all t ∈ [1, T] do 12:   ${m^{*}(t)} = {\arg \mspace{11mu} {\max_{m \in {z^{*}{(t)}}}\left\{ \frac{R_{m}(t)}{{\overset{\_}{R}}_{m}} \right\}}}$13:   end for 14:  end if 15:  Append tile (m^(*)) and zone (Z)information to MAP 16:  % Femto BS operations 17:  for all f ∈  

  do 18:   Execute DRA+ (

 (f)) 19:   if {{f ∉  

 } & Decode(MAP)} 20:   then

 (f) ← {∪t}, s.t. z^(*)(t) ≠ Z_(f) end if 21:  end for 22: end for

With reference now to FIG. 9 with continuing reference to Table 6, amethod 900 for allocating tiles corresponding to time-frequency resourceblocks of OFDMA frames dedicated to a macro cell base station tofacilitate reuse of macro cell resources by distributed femto cell basestations in accordance with one exemplary implementation of the presentinvention is illustrated. It should be understood that a macro cell basestation, such as macro cell base station 102 of FIG. 1, may beconfigured to perform method 900. It should also be understood thatmethod 900 may be employed to permit the reuse and allocation ofresources, by femto cell base stations, that were assigned to the macrocell base station, for example, in accordance with method 800. Thus, itmay be assumed here the resource split (T_(m),T_(f)) between macro andfemto cells is made available to the macro cell base station.

Method 900 may begin at step 902 in which the macro cell base stationobtains channel state information from all macro users on allsub-channels.

At step 904, the macro cell base station may provide or employ a chartdefining location zones composing the macro cell. The location zones maycorrespond to a sector. In addition, the location zones may bepreconfigured and provided to the macro cell BS by a network controlleror the macro cell BS may configure the location zones itself. Howlocation zones are configured is an important design component, as itdirectly impacts MAP overhead and has implications on both femto andmacro user throughput. The finer the division of the macro cell intozones, the more accurate is the location of the macro scheduleinformation provided to the femto cells and hence the better is theirability to estimate interference and reuse macro resources, therebyimproving femto user throughput. However, finer division of the cellinto small zones incurs larger number of bits (overhead) in the MAP toindicate zone information. More importantly, it restricts the number ofmacro users within a zone, reducing the ability to leverage MUD when themacro schedule is retained at the zone level and hence reducing themacro user throughput. Thus, the choice of the zone size has a directimpact on the macro-femto user throughput as well.

In accordance with one exemplary embodiment, the location zones may beapproximated as a square grid; however, other configurations may beemployed by those of ordinary skill in the art in view of thedescription provided herein. For a single resource tile, unlike formacro cell users, multiple femto cell user transmissions can be enableddue to spatial reuse. Thus, the utility of providing a resource tile tothe femto cells is higher. Hence, the zone size should be biased towardsthe femto cells to maximize the aggregate system utility. While makingthe zone size smaller helps achieve this goal, reducing the size beyonda certain extent will result in no appreciable gain from spatial reuse.However, the macro cell performance will continue to degrade. Hence, itis important to achieve the critical zone size, where the gain fromspatial reuse saturates. Note that the spatial reuse among femto cellsdepends on two factors: (i) femto BS density (f_(d)−femto BS per sq.km.), and (ii) interference region of a femto BS calculated at maximumtransmission power (I_(f) meters). Given that there are f_(d) femto BSper sq. km., the radius (in meters) over which there is only one femtoBS is given as

$r_{f} = {\sqrt{\frac{10^{6}}{\pi \; f_{d}}}.}$

If r_(n) is the radius of the macro cell in meters, the spatial reusecorresponding to I_(f) and r_(f) can be given as

$s_{i} \approx {\left\lfloor \left( \frac{r_{m}}{I_{f}} \right)^{2} \right\rfloor \mspace{14mu} {and}\mspace{14mu} s_{f}} \approx \left\lfloor \left( \frac{r_{m}}{r_{f}} \right)^{2} \right\rfloor$

respectively.

It should be noted that even if the interference radius is small, thespatial reuse will be upper bounded by the component (s_(f))corresponding to femto density radius if r_(f)≧I_(f), as there should befemto cell BS available to leverage the spatial reuse provided by thenetwork (s_(i)), namely s=min{s_(i),s_(r)}. Thus, the macro cell basestation may be configured to set the zone side to be z=max{I_(f),r_(f)}instead of z=I_(f), which will leverage spatial reuse to the maximumextent possible without incurring macro throughput degradation andexcessive MAP overhead.

Returning to FIG. 9, after providing the chart defining location zones,at step 906, the macro cell base station may determine the number ofmacro cell users in each location zone based on, for example, thechannel state information received from users in step 902.

At step 908, the macro cell base station may determine whether thecurrent frame is the first frame of a set of L frames. As discussedabove, L may be set to permit convergence of femto cell tile allocationand is a tunable parameter. If the current frame is the first frame inthe L set of frames, then the method may proceed to step 910. Otherwise,the method may proceed to step 914.

At step 910, the macro cell base station may allocate at least a portionof the available tiles allocated to the macro cell base station to macrocell users by considering, for each user, both a total number of macrocell users in a corresponding zone in which the user is located and amarginal utility for the user that is based on instantaneous rate andthroughput information for the user. For example, as shown in steps 4-10of Algorithm 5 of Table 6, in the first frame, the macro BS runs alocation-based scheduling algorithm to determine the set of userallocations to tiles and the corresponding zone allocations. Here, macrocell users affording a highest utility and occupancy measure forparticular tiles are allocated the tiles. In the exemplary embodimentshown in Table 6, the utility and occupancy measure is

${\frac{R_{m}(t)}{{\overset{\_}{R}}_{m}}{M_{z{(m)}}}},$

wherein R_(m)(t) is instantaneous rate information for a particularmacro cell user on a particular tile, R _(m) is the average throughputinformation for the particular macro cell user and M_(z(m)) is a totalnumber of macro cell users in a particular zone in which the particularmacro cell user is located.

At step 912, the macro cell base station may transmit to the femto cellbase stations an indication of which zones are employed by macro cellusers to enable femto cell base stations to assign at least a portion ofthe tiles to femto cell users. As stated above, the indication may beprovided in a MAP. Thus, in addition to the computed macro schedule, themacro BS appends the zone and corresponding tile information to the MAPin its frame transmissions, as shown, for example, in step 15 ofAlgorithm 5 in Table 6.

Returning to step 908, if the current frame is not the first frame inthe L set of frames, then, at step 914, the macro cell may be configuredto reallocate tiles only within zones indicated in the indication byconsidering, for each specific macro cell user, the marginal utility forthe specific macro cell user that is based on instantaneous rate andaverage throughput information for the specific macro cell user toaccount for variable channel conditions. For example, as illustrated insteps 11-13 of algorithm 5 of Table 6, the macro cell base stationreschedules the macro cell users within the zones indicated in the MAPto maximize system utility and leverage MUD. Here, system utility is

$\frac{R_{m}(t)}{{\overset{\_}{R}}_{m}},$

wherein R_(m)(t) is instantaneous rate information for a particularmacro cell user on a particular tile and R _(m) is the averagethroughput information for the particular macro cell user. The macrocell base station may restrict reallocation of tiles to users within thesame zone to permit the femto cell base stations to rely on the accuracyof the zone information appended to the MAP for L frames when allocatingtiles to femto cell users. For every period or cycle of L frames, method900 may be repeated.

Given that the femto cell BS cannot decode the MAP immediately and usethe macro schedule information to compute femto allocation for the verysame frame, every femto BS may assume itself to be an interior femtocell for the first frame (and subsequently until successful MAPdecoding) and uses only the orthogonally assigned resources (T_(f)), forexample, as shown in steps 1 and 18 of Algorithm 5 of Table 6. However,those exterior femto cell BS, such femto cell base stations 104 and 110of FIG. 1, that are able to decode the zone information in the MAP, maydetermine the set of macro tiles that can be reused by them withoutcausing interference to macro users in the zones (Z_(f)) within theirinterference range, I_(f), for example, as shown in steps 19 and 20 ofAlgorithm 5 of Table 6, where I_(f) calculated at maximum transmitpower. Here, both the macro cell and femto cell base stations maysimultaneously transmit signals on the same tiles to their respectiveusers, as long as the femto cell base stations are exterior femto cellbase stations with respect to the macro cell and macro cell users. Thetiles selected for reuse by femto cell base stations form the set ofresources that will be used for their allocations in the subsequentframes. In the subsequent frames, the macro BS retains the zone scheduleon the tiles to allow for femto cell adaptation and convergence.However, it reschedules the macro users within the zones to maximizeutility and leverage MUD, as stated above. On the other hand, theinterior femto cells use the orthogonal resources, while the exteriorones reuse macro resources without generating interference to macrousers, thereby leveraging an additional level of spatial reuse, asshown, for example, in steps 16-21 of Algorithm 5 of Table 6.

With reference now to FIG. 10 with continuing reference to FIGS. 1 and 9and Table 6, a method 1000 for allocating tiles of OFDMA frames to femtocell users in accordance with one exemplary implementation of thepresent invention is illustrated. Method 1000 may be performed by femtocell base stations simultaneously and in conjunction with theperformance of method 900 by a macro cell base station.

In accordance with this exemplary embodiment, every femto cell BSdetermines if it is an interior cell F_(i), such as, for example femtocell 112 of FIG. 1, that cannot reuse macro resources based on theinterference received from the macro BS. Thus, method 1000 may begin atstep 1002, in which femto cell BS may monitor interference received fromthe macro cell base station. Initially, the femto cell base stationassumes that it is an interior femto cell base station,

. Accordingly, the available tiles,

, from which the femto cell may select resources for use by its clientsis initially comprised of orthogonal tiles T_(f), as shown, for examplein step 1 of Algorithm 5 of Table 6. As stated above, T_(f) maycorrespond to tiles 214 of FIG. 2

At step 1004, the femto cell base station determines whether it is aninterior or exterior femto cell base station. For example, if theinterference is at or above a threshold, the femto cell base station isdetermined to be an interior femto cell. Conversely, if the interferenceis below the threshold, femto cell base station is determined to be anexterior femto cell. The method steps described below are performed forevery frame in the set of L frames. As such, at step 1006, the femtocell base station determines if a new frame is reached and proceeds tostep 1008 when a new frame is reached.

At step 1008, the femto cell base station may execute a distributedspatial reuse scheme on tiles

determined from a previous frame. For example, as shown in step 18 ofAlgorithm 5 of Table 6, the DRA+ scheme, described above with respect tomethod 300, may be executed on tiles within the set

to allocate resources to its users. Alternatively, DRA may be employedas the resource allocation scheme.

At step 1010, the femto cell base station may determine whether it isusing tiles T_(f) assigned to femto cells. As discussed above, for thecoupled architecture, it is optimal for only the interior femto cellusers to use the orthogonal channels allocated to the femto cell basestations, while the exterior femto users only employ reused tilesallocated to the macro cell base station. Thus, in this exemplaryembodiment, T_(f) is reserved for interior femto cell base stations.Accordingly, if the femto cell base station is using tiles from T_(f),then the method may proceed to step 1012.

At step 1012, the femto cell base station determines whether it candecode the indication in, for example, a MAP. If the femto cell cannotdecode the indication or if the femto cell is an interior femto cell,then the femto cell base station continues to operate as an interiorcell on T_(f) tiles. If the femto cell base station can decode the MAPand it is an exterior cell, determined for example in step 104, then thefemto cell base station may determine the set of macro tiles to reuse atstep 1014. For example, as illustrated in steps 19-20 of algorithm 5 ofTable 6, the femto cell base station maps macro cell tiles to the set

by considering zone and tile information provided in the MAP. Forexample, the femto cell base station maps a set of macro cell tiles to

which are not allocated to macro users in the zones (Z_(f)) within itsinterference range, I_(f). Thereafter, step 1006 may be repeated and themethod may proceed until a new set of L frames is reached, after whichmethod 1000 may be iterated. In addition, at step 1014, the femto cellbase station may distribute allocation messages to femto cell users topermit communication on macro cell tiles within

.

Returning to step 1010, if the femto cell base station is not usingT_(f) tiles, then the method may repeat steps 1006 and 1008, as theFemto cell is an exterior femto cell that is already using macro cellresources in accordance with zone information received from the macrocell base station. Thereafter, step 1006 may be repeated and the methodmay proceed until a new set of L frames is reached, after which method1000 may be iterated.

It can be shown that with increasing L, the performance gain of LRA overuser-based macro cell resource allocation is equivalent to that of aselection diversity system of order M*, where M*ε[1,K_(m)], is theoccupancy size of the zone selected in LRA and K_(m) is the macropopulation size. Further, LRA retains good fairness properties ofproportional fairness (PF) by virtue of being a weighted PF scheme. Theweight is important in restoring the loss in MUD from the macroschedule.

Exemplary implementations of the present invention discussed aboveprovide efficient resource management solutions for OFDMA-based femtocells. A completely distributed and simple-to-implement resourceallocation solution for femto cells has been described with performanceguarantees. Optimal resource allocation between macro and femto cellshas also been addressed. Further, a novel location-based resourcemanagement solution for leveraging maximal spatial reuse from femtocells by allowing femto cells reuse macro resources has been disclosed.Comprehensive evaluations indicate that with carefully designed resourcemanagement solutions applied in accordance with the above-describedteachings, femto cells have the potential to increase the systemperformance by two folds over static resource allocation schemes.

It should be understood that although the present invention has beendescribed with respect to femto cell base stations, the presentinvention, except for the use of a cable backhaul, may equivalently beapplied to relay stations in lieu of femto cell base stations, as notedabove.

It should also be understood that embodiments described herein may beentirely hardware or including both hardware and software elements. In apreferred embodiment, the present invention is implemented in hardwareand software, which includes but is not limited to firmware, residentsoftware, microcode, etc.

Embodiments may include a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. A computer-usable or computer readable medium may include anyapparatus that stores the program for use by or in connection with theinstruction execution system, apparatus, or device. Any method describedherein may be implemented in the program. The medium can be magnetic,optical, electronic, electromagnetic, infrared, or semiconductor system(or apparatus or device). The medium may include a computer-readablemedium such as a semiconductor or solid state memory, magnetic tape, aremovable computer diskette, a random access memory (RAM), a read-onlymemory (ROM), a rigid magnetic disk and an optical disk, etc.

Having described preferred embodiments of systems and methods (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artin light of the above teachings. It is therefore to be understood thatchanges may be made in the particular embodiments disclosed which arewithin the scope and spirit of the invention as outlined by the appendedclaims. Having thus described aspects of the invention, with the detailsand particularity required by the patent laws, what is claimed anddesired protected by Letters Patent is set forth in the appended claims.

1. In a wireless network including a first base station and a secondbase station, a method implemented in the first base station, the methodcomprising: upon a change in a load condition, receiving from the secondbase station load information through backhaul in order to coordinatethe first base station and the second base station; allocating, to thesecond base station, one or more tiles corresponding to one or moretime-frequency resource blocks of orthogonal frequency division multipleaccess (OFDMA); and allocating remaining tiles to the first basestation, wherein no transmission from the first base station is includedin said one or more tiles so that interference between the first basestation and the second base station is mitigated.
 2. The method as inclaim 1, wherein the first base station comprises a macro cell basestation, a small cell base station, or a distributed extension cell basestation.
 3. The method as in claim 2, wherein the small cell basestation comprises a relay station or a femto cell base station.
 4. Themethod as in claim 2, wherein the extension cell base station comprisesa relay station or a femto cell base station.
 5. The method as in claim1, wherein the second base station comprises a macro cell base station,a small cell base station, or a distributed extension cell base station.6. The method as in claim 5, wherein the small cell base stationcomprises a relay station or a femto cell base station.
 7. The method asin claim 5, wherein the extension cell base station comprises a relaystation or a femto cell base station.
 8. The method as in claim 1,wherein the load information includes an interference condition.
 9. Themethod as in claim 1, wherein the method is located in the first basestation.
 10. The method as in claim 1, wherein the method is located ina network controller.
 11. The method as in claim 1, wherein the resourceblocks comprises time slots.
 12. The method as in claim 1, wherein theresource blocks comprises frequency or sub-channel slots.
 13. In awireless network including a first base station and a second basestation, the first base station comprising: a receiving unit to, upon achange in a load condition, receive from the second base station loadinformation through backhaul in order to coordinate the first basestation and the second base station; a first allocation unit toallocate, to the second base station, one or more tiles corresponding toone or more time-frequency resource blocks of orthogonal frequencydivision multiple access (OFDMA); and a second allocation unit toallocate remaining tiles to the first base station, wherein notransmission from the first base station is included in said one or moretiles so that interference between the first base station and the secondbase station is mitigated.
 14. In a wireless network including a firstbase station and a second base station, a method implemented in thesecond base station, the method comprising: upon a change in a loadcondition, transmitting to the first base station load informationthrough backhaul in order to coordinate the first base station and thesecond base station, wherein the first base station allocates, to thesecond base station, one or more tiles corresponding to one or moretime-frequency resource blocks of orthogonal frequency division multipleaccess (OFDMA), and allocates remaining tiles to the first base station,and wherein no transmission from the first base station is included insaid one or more tiles so that interference between the first basestation and the second base station is mitigated.
 15. The method as inclaim 14, wherein the first base station comprises a macro cell basestation, a small cell base station, or a distributed extension cell basestation.
 16. The method as in claim 15, wherein the small cell basestation comprises a relay station or a femto cell base station.
 17. Themethod as in claim 15, wherein the extension cell base station comprisesa relay station or a femto cell base station.
 18. The method as in claim14, wherein the second base station comprises a macro cell base station,a small cell base station, or a distributed extension cell base station.19. The method as in claim 18, wherein the small cell base stationcomprises a relay station or a femto cell base station.
 20. The methodas in claim 18, wherein the extension cell base station comprises arelay station or a femto cell base station.
 21. The method as in claim14, wherein the load information includes an interference condition. 22.The method as in claim 14, wherein the method is located in the firstbase station.
 23. The method as in claim 14, wherein the method islocated in a network controller.
 24. The method as in claim 14, whereinthe resource blocks comprises time slots.
 25. The method as in claim 14,wherein the resource blocks comprises frequency or sub-channel slots.26. In a wireless network including a first base station and a secondbase station, the second base station comprising: a transmitting unitto, upon a change in a load condition, transmit to the first basestation load information through backhaul in order to coordinate thefirst base station and the second base station, wherein the first basestation allocates, to the second base station, one or more tilescorresponding to one or more time-frequency resource blocks oforthogonal frequency division multiple access (OFDMA), and allocatesremaining tiles to the first base station, and wherein no transmissionfrom the first base station is included in said one or more tiles sothat interference between the first base station and the second basestation is mitigated.
 27. A method implemented in a wireless networkincluding a first base station and a second base station, the methodcomprising: upon a change in a load condition, transmitting from thesecond base station to the first base station load information throughbackhaul in order to coordinate the first base station and the secondbase station; allocating, to the second base station, one or more tilescorresponding to one or more time-frequency resource blocks oforthogonal frequency division multiple access (OFDMA); and allocatingremaining tiles to the first base station, wherein no transmission fromthe first base station is included in said one or more tiles so thatinterference between the first base station and the second base stationis mitigated.
 28. A wireless network comprising: a first base station;and a second base station, wherein, upon a change in a load condition,the wireless network transmits from the second base station to the firstbase station load information through backhaul in order to coordinatethe first base station and the second base station, wherein the wirelessnetwork allocates, to the second base stations, one or more tilescorresponding to one or more time-frequency resource blocks oforthogonal frequency division multiple access (OFDMA), and allocatesremaining tiles to the first base station, and wherein no transmissionfrom the first base station is included in said one or more tiles sothat interference between the first base station and the second basestation is mitigated.